Critical Evaluation of Tuberculosis Diagnostic Tests in Low- and High- Burden Settings

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1 Critical Evaluation of Tuberculosis Diagnostic Tests in Low- and High- Burden Settings by John Metcalfe Dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Epidemiology in the Graduate Division of the University of California, Berkeley Committee in charge: Arthur Reingold, Chair Lee Riley Maya Petersen Eva Harris Fall 2012

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3 Abstract Critical Evaluation of Tuberculosis Diagnostic Tests in Low- and High-Burden Settings by John Metcalfe Doctor of Philosophy in Epidemiology University of California, Berkeley Professor Arthur Reingold, Chair Tuberculosis (TB) is the second leading cause of death from an infectious disease worldwide and remains a major public health challenge, particularly in resource limited settings. Given the effectiveness of current treatment regimens, enhanced detection of disease in both low- and highburden settings will be critical in making progress towards TB elimination. As TB case rates have declined in high-income settings, TB control has centered on finding and treating individuals with non-infectious latent TB infection (LTBI) in order to prevent reactivation infectious TB disease. Since 2005, the Centers for Disease Control and Prevention has recommended use of interferon-γ release assays (IGRAs), in vitro immuno-diagnostic tests that measure effector T-cell mediated interferon-gamma (IFN-γ) response to M. tuberculosis specific antigens, could be used for targeted screening of LTBI in all circumstances in which the tuberculin skin test (TST) is used. We found that higher quantitative IFN-γ results were associated with active tuberculosis and added clinical value to a prediction model incorporating conventional risk factors; however, in all settings and especially within low- and middle-income countries, IGRAs are inadequate rule-out or rule-in tests for active TB. Although IGRAs are widely used in high-income countries and numerous studies have evaluated their diagnostic performance for detection of LTBI, there is limited data on the precision of IGRA results. In the largest precision study of an IGRA to date, we found considerable variability in TB response measured by QuantiFERON-TB Gold In-Tube (QFT-GIT, Cellestis, Australia); test results should be interpreted cautiously among low-risk individuals with positive TB response less than 0.59 IU/ml. In contrast to low TB burden, high income settings, TB control in low income settings focuses on early detection and treatment of individuals with active, infectious TB. In the WHO African Region, the incidence of multidrug resistant TB (MDR-TB) has tripled in the past 20 years and poses a major risk to regional TB control programs. Accurate, timely, and affordable drug susceptibility testing for patient management and in support of surveillance programs is urgently needed. In a politically unstable, high HIV prevalence region of southern Africa, we validated use of a low-cost, accelerated phenotypic method for MDR-TB detection, and provide the first report of the prevalence of MDR-TB from this country in 17 years. 1

4 Table of Contents Introduction and Rationale..1 Use of Interferon-γ Release Assays in the Diagnosis of Active Tuberculosis Chapter 1. Evaluation of Quantitative Interferon-γ Response for Risk Stratification of Patients Suspected of Having Active Tuberculosis..3 Chapter 2. Interferon-Gamma Release Assays for Diagnosis of Active Pulmonary Tuberculosis in Low- and Middle-Income Countries: Systematic Review and Meta-Analysis..17 Use of interferon-γ release assays for latent M. tuberculosis infection in low-incidence settings Chapter 3. Test Variability of the Quantiferon-TB Gold In-Tube Assay in Clinical Practice 38 Detection of Multidrug Resistant Tuberculosis in High-burden, High HIV-prevalence Regions Chapter 4. Use of Microscopic-observation Drug Susceptibility Assay among Patients Suspected of Having Drug-Resistant Tuberculosis in Harare, Zimbabwe 51 Chapter 5. Evaluation of Patients Suspected of Having Multidrug Resistant Tuberculosis in Zimbabwe.62 Concluding Remarks References...68 i

5 Introduction and Rationale Tuberculosis (TB) is an infectious airborne disease caused by the bacillus Mycobacterium tuberculosis, one of the oldest known pathogens in humans. TB is the second leading cause of death from an infectious disease worldwide (after the human immunodeficiency virus (HIV)), and remains a major public health problem, particularly in resource limited settings. A six-month regimen of four first-line drugs (isoniazid, rifampicin, ethambutol and pyrazinamide) cures ~90% of cases and has been available since the 1980s; yet, in 2011 the global burden of TB remained substantial, with nearly 9 million new cases and 1.4 million deaths from TB. In high-income settings such as the United States, the incidence of TB has declined steadily since the early 1990s. However, declines have occurred disproportionately among U.S.-born individuals, so that the majority of TB (and TB/HIV) cases in the U.S. now occur among immigrants from high TB burden countries. As TB case rates have declined, a major goal of TB control in high-income settings has centered on finding and treating individuals with noninfectious latent TB infection (LTBI), in order to prevent reactivation infectious TB disease. Traditionally, the tuberculin skin test (TST), the oldest clinical diagnostic test still in use today has been used to identify individuals with LTBI. Since 2005, the Centers for Disease Control and Prevention (CDC) has endorsed use of interferon-γ release assays (IGRAs), in vitro immunodiagnostic tests that measure effector T cell-mediated interferon-gamma response to synthetic Mycobacterium tuberculosis specific polypeptides. In this dissertation, I examine two major issues arising out of the widespread use of IGRAs. First, IGRAs, as with the TST, have been inappropriately employed by clinicians in algorithms for diagnosis of active TB. Commercial IGRAs have been a lucrative market for the private sector in countries with a high burden of TB disease and emerging economies (e.g., India, South Africa, Brazil, and China). Two reports in this dissertation (Chapters 1 and 3) address this issue in low- and high-tb burden settings, respectively. I presented this work at a World Health Organization (WHO) Expert Group meeting, resulting in a recommendation by the WHO against the use of IGRAs for active TB diagnosis in high burden settings. Second, when appropriately used for LTBI diagnosis, there is lack of convincing evidence in support of defining test cut points for positivity (in low-risk populations) or test conversion (in any population) to guide initiation of treatment for LTBI, an important issue given the non-trivial nature of LTBI treatment (the most common treatment regimen entails nine months of a potentially hepatotoxic medication). Using a linear mixed effects model fit to the numerical IFN-γ values, Chapter 3 seeks to provide data concerning possible cut points that can be used in clinical practice. In contrast to high income settings, TB control in low income settings focuses predominantly on early detection and treatment of individuals with active, infectious TB; in these settings, despite high-quality evidence supporting the use of isoniazid preventive therapy among persons with HIV co-infection, treating LTBI is considered low-priority. Although new cases of TB globally have been falling for several years at a slow but steady rate (i.e., a decrease of 2.2% between 2010 and 2011), multidrug resistant (MDR) TB is emerging as a major challenge. The number of 1

6 cases of MDR-TB worldwide is increasing, surpassing 300,000 incident cases in Yet, less than one in five of these cases are reported to national TB programs, and fewer than 0.5% are treated according to standards of care in the United States. In the WHO African Region, the incidence of MDR has tripled in the past 20 years and poses a major risk to regional TB control programs. Yet, fewer than half of the 46 countries in the WHO African Region have provided representative data concerning the prevalence of drug resistance among M. tuberculosis strains, and only ten have reported such data since Accurate, timely, and affordable drug susceptibility testing (DST) of M. tuberculosis in support of surveillance and patient management is urgently needed in high burden countries. Chapter 4 of this dissertation deals with identification of MDR-TB using low-cost, accelerated phenotypic methods in a politically unstable region of southern Africa with a high prevalence of HIV infection, and Chapter 5 presents the first report of the prevalence of MDR-TB from this country in 17 years. 2

7 Chapter 1: Evaluation of Quantitative Interferon-γ Response for Risk Stratification of Patients Suspected of Having Active Tuberculosis Scientific Knowledge on the Subject The role of interferon-γ release assays (IGRAs) in the evaluation of patients suspected of having active tuberculosis is controversial. Whether IGRAs improve classification of individuals who are suspected of having tuberculosis and whose sputum smear for acid fast bacilli is negative into clinically relevant risk categories has not been examined. What This Study Adds to the Field Quantitative interferon-γ levels measured by QuantiFERON -TB Gold improves risk stratification of smear-negative active tuberculosis suspects when added to objective clinical and demographic risk factors. However, this benefit is attenuated when the judgment of experienced clinicians is also taken into account. Abstract Rationale: The contribution of interferon-γ release assays (IGRAs) to appropriate risk stratification of patients suspected of having tuberculosis has not been studied. Objective: To determine whether addition of quantitative IGRA results to a prediction model incorporating clinical criteria improves risk stratification of smear-negative tuberculosis suspects. Methods: Clinical data from tuberculosis suspects evaluated by the San Francisco Department of Public Health Tuberculosis Control Clinic from March 2005 to February 2008 were reviewed. We excluded tuberculosis suspects who were acid fast bacilli smear-positive, HIV-infected, or under 10 years of age. We developed a clinical prediction model for culture-positive disease and examined the benefit of adding quantitative interferon-γ results obtained using QuantiFERON - TB Gold. Main Results: Of 660 patients meeting eligibility criteria, 65 (10%) had culture-proven tuberculosis. The odds of active tuberculosis increased by 7% (95% CI 3-11%) for each doubling of interferon-γ level. The addition of quantitative interferon-γ results to objective clinical data significantly improved model performance (c-statistic 0.71 vs. 0.78, p<0.001) and correctly reclassified 32% of tuberculosis suspects (95% CI 11-52%, p<0.001) into higher or lower risk categories. However, quantitative interferon-γ results did not significantly improve appropriate risk reclassification beyond that provided by clinician assessment of risk (5%, 95% CI -7 to +22%, p=0.14). Conclusion: Higher quantitative interferon-γ results were associated with active tuberculosis, and added clinical value to a prediction model incorporating conventional risk factors. While this 3

8 benefit may be attenuated within centers with highly experienced clinicians, the predictive accuracy of quantitative interferon-γ levels should be evaluated in other settings. Introduction Interferon-γ release assays (IGRAs) are in vitro immuno-diagnostic tests that measure effector T- cell mediated interferon-gamma (IFN-γ) response to M. tuberculosis specific antigens. IGRAs are as sensitive and more specific than the tuberculin skin test for detecting latent tuberculosis infection (LTBI) (1, 2) and have better correlation with gradient of M. tuberculosis exposure (3-8). In 2005, the Centers for Disease Control and Prevention recommended that QuantiFERON TB-Gold (QFT-G, Cellestis, Carnegie, Australia) - the first FDA-approved, commercially available IGRA in widespread use - could be used for targeted screening of LTBI in all circumstances in which the tuberculin skin test (TST) is used (9). While the advantages of IGRAs in diagnosing LTBI are well established, their role in evaluating patients suspected of having tuberculosis suspects remains unclear. IGRAs have variable, though often suboptimal, sensitivity and specificity for diagnosing active tuberculosis (1, 2, 10-16). To date, with the exception of studies examining these assays in parallel with the TST (11, 17), IGRAs have not been considered in light of conventional risk factors for active disease. In addition, whether IGRAs improve prediction of an individual patient s likelihood of having active tuberculosis has not been examined. Acid fast bacilli (AFB) smear-positive tuberculosis suspects can often be triaged with relative ease. However, in suspects whose sputa or other tissue are smear-negative for AFB, clinicians use demographic and clinical risk factors, symptoms, and chest radiograph findings to classify patients into low, intermediate, or high risk categories for active tuberculosis. Patients classified as being at high risk are typically initiated on anti-tuberculosis therapy, whereas treatment is withheld for low risk patients. In this study, we use novel risk reclassification methods (18) to assess whether addition of quantitative IFN-γ response measured by QuantiFERON TB-Gold (QFT-G, Cellestis, Carnegie, Australia) to routine clinical evaluation improves risk stratification of smear-negative pulmonary and extrapulmonary tuberculosis suspects. Some of the results of these studies have been previously reported in abstract form (19). Methods Study Population The San Francisco Department of Public Health (SFDPH) operates a central Tuberculosis Control Clinic that routinely screens contacts, immigrants and refugees, as well as hospitalized, private, and community health center patients for LTBI and active tuberculosis in accordance with American Thoracic Society (ATS), Centers for Disease Control and Prevention (CDC) and Infectious Diseases Society of America (IDSA) guidelines (20). The target population for this study includes AFB smear negative pulmonary or extrapulmonary tuberculosis suspects who presented to the SFDPH Tuberculosis Control Clinic between March 2005 and February 2008 and had QFT-G performed as part of their initial evaluation. Patients with QFT-G results that were (1) indeterminate; (2) performed greater than 14 days prior to or 14 days following their initial clinic visit; or (3) performed more than 7 days into a course of tuberculosis treatment were 4

9 excluded. In addition, patients aged less than 10 years (in whom adult-type, non-paucibacillary disease is uncommon) (21, 22); with a positive AFB smear examination; known diagnosis of active tuberculosis at presentation; known HIV-infection; or with a final diagnosis of culturenegative tuberculosis were excluded. Demographic and clinical information was extracted from the SFDPH Tuberculosis Control Clinic electronic database. QFT-G assays were performed at the SFDPH laboratory according to the manufacturer s instructions (23). Patients were considered to have active tuberculosis only when there was culture confirmation of M. tuberculosis. The study protocol was approved by the Committee for Human Research at the University of California, San Francisco. Statistical Methods The analysis included the following steps. First, a novel model selection procedure, the Deletion/Substitution/Addition (DSA) algorithm (24), was used to select the optimal prediction model for culture-confirmed tuberculosis using standard clinical and demographic variables; the following limits for the DSA algorithm were set: third order polynomials, second order interaction terms, and maximum model size of 10 variables. Covariates were considered for inclusion in the model based on previous studies of risk factors for active tuberculosis and included the following: age, gender, foreign birth, homelessness, contact with an active tuberculosis case, previous history of active tuberculosis, predisposing medical condition (e.g., diabetes mellitus, silicosis, cancer, or condition requiring use of immunosuppressive medications), symptoms of active tuberculosis (e.g., night sweats, weight loss, or cough), and findings on initial chest radiograph. We also performed a secondary analysis in which clinician suspicion for active disease at the time of patient evaluation (classified as low, intermediate, or high) was added to the baseline clinical prediction model generated by DSA. Second, patients were classified as low (< 5%), intermediate (5-20%), or high risk (>20%) for active tuberculosis based on the probability assigned by the baseline clinical prediction model (this classification was distinct from clinician suspicion for active disease described above). The lower and upper probability cut-points for tuberculosis risk categories were selected based on the assumption that empiric tuberculosis treatment would be withheld when the probability of active tuberculosis was below the lower risk threshold (low risk) and prescribed when the probability was above the higher risk threshold (high risk). Sensitivity analyses were performed using alternate low and high risk thresholds of 2.5% and 10%, and 10% and 30%. Third, quantitative IFN-γ results were added to the clinical prediction model. Performance of the prediction models with and without quantitative IFN-γ results were then compared using receiver-operator characteristic (ROC) analysis (25) and net reclassification index (NRI) (18). Based upon the pre-specified risk thresholds, the NRI reflects the net proportion of patients with culture-positive tuberculosis reclassified into a higher risk category, plus the net proportion of patients without culture-positive tuberculosis reclassified into a lower risk category (NRI = [P(up D =1) P(down D =1)] [P(up D =0) P(down D =0)]). Final estimates of NRI and AUC were obtained using 10-fold cross-validation. Bootstrap confidence intervals for the NRI estimate are reported based on 1000 re-sampling iterations. All p-values were two-sided with α=0.05 as the significance level. All analyses were performed using Stata 10 (Stata Corporation, College Station, Texas) and R, version (R Project for Statistical Computing). 5

10 Results 0f 1000 active tuberculosis suspects who had a QFT-G performed as part of their evaluation, 660 were included in the analysis (Figure 1). Of the 660 suspects, 630 (95%) had sputa and 30 (5%) had other tissue sent for AFB smear and culture as part of their diagnostic evaluation. Sixty-five (10%) patients were ultimately diagnosed with culture confirmed tuberculosis, of whom 14 (22%) had extrapulmonary tuberculosis. Median IFN-γ level was similar in patients with pulmonary and extrapulmonary disease (1.1 IU/ml vs. 1.0 IU/ml, p=0.89). The study population was predominantly male and foreign-born. Cases were more likely than non-cases to have weight loss, night sweats, and chest radiographs with evidence of active disease on presentation, and less likely to have a history of prior active tuberculosis (Table 1). Median IFN-γ level was significantly higher in patients with tuberculosis compared to those without (1.1 IU/ml vs IU/ml, p<0.001), and higher IFN-γ levels were associated with increased odds of active tuberculosis (OR 1.07 (95% CI ) for each doubling of IFN-γ level). For example, a patient with a quantitative IFN-γ result of 10 IU/ml had a 41% (95% CI 16-66%) increased odds of active tuberculosis relative to a patient with test results at the manufacturer-recommended cutpoint of 0.35 IU/ml. 85% of cases had quantitative IFN-γ results in the upper three quintiles of IFN-γ concentration ( 0.23 IU/ml), while only 6% of cases were in the lowest quintile (< 0.04 IU/ml) (Table 1). Sensitivity and specificity of QFT-G for active tuberculosis at the manufacturer-recommended cut-point were 72% and 47%, and positive and negative predictive values were 13% and 89%, respectively. A tuberculin skin test was performed in 117 (18%) patients prior to QFT-G measurement. There was no difference in the proportion of patients with culture-confirmed tuberculosis among those who did and did not have a tuberculin skin test performed prior to QFT-G (p=0.41). Clinical Prediction Model The baseline prediction model including objective demographic and clinical predictors classified 182 (28%) patients into low risk, 407 (62%) into intermediate risk, and 71 (11%) into high risk categories. The presence of new infiltrate, pleural effusion, or lymphadenopathy on chest radiograph was most predictive of active tuberculosis (Table 2). Quantitative IFN-γ Results and Risk Reclassification The addition of quantitative IFN-γ results to the baseline prediction model including demographic and clinical predictors significantly improved model accuracy (AUC 0.71 ( ) vs ( ), p<0.001) (Table 2) and 32% (95% CI 11-52%, p<0.001) of tuberculosis suspects were correctly reclassified into higher or lower risk categories (Table 3a). In comparison to the clinical model alone, both case reclassification (14 more cases classified as high risk and 5 fewer as low risk) and non-case reclassification (90 more non-cases designated as low-risk and only 7 more classified as high-risk) were improved. Results were similar when alternate thresholds were used to define risk categories (Supplementary Table 1). Findings on chest radiograph remained the strongest predictor of active tuberculosis. Secondary Analysis 6

11 We performed a secondary analysis to determine whether quantitative IFN-γ levels improved risk reclassification beyond a prediction model including clinician suspicion. First, we evaluated whether a similar benefit in risk reclassification occurred when clinician suspicion, rather than quantitative IFN-γ level, was added to the baseline prediction model including objective demographic and clinical data. When clinician suspicion was added to the baseline model, accuracy increased (AUC 0.71 (95% CI ) vs (95% CI ), p<0.001) and 45% of tuberculosis suspects (95% CI 23-80%, p<0.001) were appropriately reclassified into higher or lower risk categories (data not shown). Next, addition of quantitative IFN-γ results to this expanded model including clinician suspicion significantly increased accuracy (AUC 0.82 ( ) vs ( ), p=0.02), but not net reclassification index (NRI 4%, 95% CI , p=0.14). Improved prediction among tuberculosis cases was outweighed by worse performance among non-cases (Table 3b). The addition of QFT-G results at the manufacturerrecommended cut-point of 0.35 IU/mL in place of quantitative IFN-γ levels did not materially affect results obtained in either the primary or secondary analysis. To further explore performance in cases and non-cases, we examined individual patients risk before and after quantitative IFN- γ level was added to the model. The majority of culture-proven cases showed an appropriate increase in predicted risk with addition of quantitative IFN- γ results (Figure 2a). However, both decreased (appropriate) and increased (inappropriate) risk prediction was common among non-cases (Figure 2b). Discussion In this study, we found that quantitative IFN-γ results significantly improved risk stratification of smear-negative pulmonary and extrapulmonary tuberculosis suspects when added to objective clinical and demographic risk factors. However, this benefit in prediction became attenuated when clinician suspicion was taken into account. These findings indicate that IFN-γ levels obtained from QFT-G, at either the manufacturer-recommended cut-point or as a quantitative measure, are unlikely to influence clinical management of active tuberculosis suspects attending centers with highly experienced clinicians in low incidence settings. Risk prediction has long been used in the cardiovascular (26, 27) and cancer (28) fields to improve precision of diagnoses and inform decisions about treatment. Published literature to date assessing IGRA performance has been limited to considerations of sensitivity, specificity, and predictive value, though these measures alone do not describe the predictive accuracy of these assays or the extent to which they improve upon readily available clinical information (29). In the absence of an established risk prediction model for AFB smear-negative tuberculosis, we utilized the Deletion/Substitution/Addition (DSA) routine (24) to identify the optimal prediction model. This state-of-the-art procedure considers non-linear terms and all possible interactions between predictors. Simultaneously, DSA avoids model overfitting through repeated crossvalidation. The models generated in this study demonstrate moderate to good discrimination, similar to the Framingham Risk Score for prediction of mortality from coronary heart disease (27, 30). Previous studies examining quantitative QFT-G results have shown improved sensitivity when using cut-points lower than that suggested by the manufacturer (14, 31, 32). However, cut-points selected from AUC analysis are influenced by disease prevalence in the population being 7

12 studied, give equal weight to false positive and false negative test results, and may misclassify individuals whose test result falls near the selected cut-points (33). Our analyses incorporated IFN- γ levels as a continuous measure, reported diagnostic benefit in light of conventional risk factors, and used novel reclassification methods that allow QFT-G results to be considered in the context of standard clinical decision-making. Our overall conclusions weight the net reclassification results more heavily than improvements in discrimination represented by increases in AUC. While broadly used as a summary measure of test performance, the area under the receiver-operator characteristic curve (AUC) does not focus on actual risk probabilities and their relation to clinical decision-making, and is thus limited in its clinical relevance and utility for evaluating risk prediction models (29, 34). The changes in predicted risk of active tuberculosis following consideration of quantitative IFNγ results were not uniform. Among intermediate and high risk patients in whom active TB was eventually ruled out, the addition of quantitative interferon-γ results led to clinically significant decreases in risk probabilities whether or not clinical suspicion was also included in the prediction model. These findings support previous work emphasizing a high negative predictive value for QFT-G (11). However, approximately one-quarter of low risk suspects in whom tuberculosis was eventually ruled out were inappropriately reclassified as intermediate risk after consideration of quantitative IFN-γ results. The possibility that quantitative IFN-γ results have increased clinical utility in intermediate and high risk tuberculosis suspects warrants further study. Our study has several limitations. First, net reclassification index results depend heavily upon both the base prediction model and choice of risk categories. We recognize that addition of IFNγ to suboptimal base models could produce large improvements in both discrimination and risk reclassification. We utilized novel methods to optimize our prediction models and their performance compares well with other well-accepted risk prediction models (27, 30). In addition, our risk cut-points were pre-specified, and sensitivity analyses of alternate cut-points did not modify our findings. Second, clinician suspicion, as used in our expanded clinical model, could have been influenced in some cases by QFT-G results. This is unlikely to have materially affected our analysis, as 85% of all QFT G results were not available at the time of clinical evaluation, and quantitative interferon-gamma results are not reported by the SFDPH laboratory. The dramatic improvement in model performance with addition of clinician suspicion, however, indicates that crucial information is obtained in the work up process beyond our measured covariates. Future prospective studies should attempt to better define these factors. Third, the test characteristics of QuantiFERON-TB Gold In-Tube (QFT-G-IT), the most recent generation of this assay, may differ from QFT-G as used in this study. Last, our analysis is most relevant to tuberculosis referral centers with experienced clinicians operating in low incidence settings. In conclusion, quantitative IFN-γ results obtained from QFT-G improved clinical evaluation of tuberculosis suspects compared to objective criteria. But in our highly experienced tuberculosis control clinic, subjective assessment of risk by clinicians performed even better. Further studies are needed to examine whether quantitative IGRA results have benefit beyond routine clinician evaluation in other settings. ACKNOWLEDGEMENTS The authors would like to thank the staff at San Francisco Department of Public Health, Tuberculosis Control Section. 8

13 Table 1. Description of AFB Smear Negative Active Tuberculosis Suspects Characteristic Total Non-Cases (n=595) Culture- Positive Cases (n=65) p-value Age (yrs); median (IQR) 54 (43-64) 54 (44-65) 50 (36-62) 0.06 Male, % Race/Ethnicity, % White African American Asian Hispanic US Born, % Positive QFT-G Result,* % <0.01 IFN-γ Concentration, 0.46 ( ( (0.36- IU/ml (IQR) 2.42) 2.38) 3.77) <0.01 Quintiles of IFN-γ Concentration (IU/ml), % < < Active Disease on CXR, % <0.001 Clinical Symptoms, % Night sweats or weight loss <0.001 Cough Hemoptysis Previous Active TB, % Contact with Active TB Case, % Predisposing Medical Condition, % Homelessness, % BCG Vaccination, % Diabetes Mellitus, % Clinician Suspicion for Active TB at Initial Evaluation, % Low <0.001 Intermediate High

14 Values are expressed as percentages unless otherwise stated. * Positive at the manufacturer-recommended cut-point of 0.35 IU/ml. Predisposing medical condition: diabetes mellitus, silicosis, cancer, or condition requiring use of immunosuppressive medications. Definition of abbreviations: IFN-γ = Interferon-gamma; IU/ml = International units per milliliter; IQR = Interquartile range; US = United States; QFT-G = QuantiFERON -TB Gold; BCG = Bacillus Calmette- Guérin vaccine; TB = tuberculosis; CXR = chest radiograph. 10

15 Table 2. Coefficients and Summary Statistics for Prediction Models Baseline Clinical Prediction Model Baseline Prediction Model with IFN-γ Results Baseline Prediction Model with Clinician Suspicion Baseline Prediction Model with Clinician Suspicion and IFN-γ Results CXR, Active Disease* Night sweats or weight loss Previous Active Disease US Birth Foreign Birth, 2 years in US Foreign Born, 3-12 years in US Contact to Active Case High Clinical Suspicion Intermediate Clinical Suspicion Quantitative IFN-γ Result (effect size per each doubling, IU/ml) AIC AUC 0.71 ( ) 0.79 ( ) 0.82 ( ) 0.86 ll ( ) Note: all results displayed in Table 3 are cross-validated. * Reference category: inactive disease or normal CXR Reference category: Foreign born, >12 years in U.S. Reference category: low clinical suspicion Significant difference (p<.001) between this model and previous model without quantitative IFN-γ results. ll Significant difference (p=.02) between this model and previous model without quantitative IFN-γ results. Definition of abbreviations: IFN-γ = Interferon-gamma; IU/ml = International units per milliliter; US = United States; CXR = chest radiograph; AUC = Area under the receiver operating curve, the probability that a randomly selected case will have a higher test value than a randomly selected noncase; a perfect test has an area under the curve of 1.0, while a worthless test has an area of 0.5; AIC = Akaike information criterion, a measure of the goodness of fit of a statistical model with lower values indicating better fit. 11

16 Table 3: Risk Reclassification Following Incorporation of IFN-γ Results A. Comparison to Baseline Clinical Prediction Model Model with Clinical Predictors Alone Model with Clinical Predictors and Quantitative IFN-γ Results In 65 patients who developed culture-positive disease: 5% risk 5-20% risk >20% risk Total No. 5% risk % 5-20% risk % >20% risk % Total No In 595 patients who ruled out for active tuberculosis: 5% risk % 5-20% risk % >20% risk % Total No Per cent Appropriately Reclassified Net reclassification improvement = 31.9% (p <.001) [Reclassification among patients who developed culture-positive disease: 20% (p <.01), reclassification among patients who ruled out for active tuberculosis: 11.9% (p<.001)]. B. Comparison to Expanded Clinical Prediction Model Model with Clinical Predictors Alone Model with Clinical Predictors and Quantitative IFN-γ Results In 65 patients who developed culture-positive disease: 5% risk 5-20% risk >20% risk Total No. 5% risk % 5-20% risk % >20% risk % Total No In 595 patients who ruled out for active tuberculosis: 5% risk % 5-20% risk % >20% risk % Total No Per cent Appropriately Reclassified 12

17 Net reclassification improvement = 3.7% (p =.31) [Reclassification among patients who developed culture-positive disease: 18.5% (p <.01), reclassification among patients who ruled out for active tuberculosis: -14.8% (p = 1)]. 13

18 Figure 1. Study Flow Diagram Active tuberculosis suspects evaluated with QuantiFERON -TB Gold, March February 2008, n= 1000 Eligible QuantiFERON -TB Gold, n=827 (83%) Ineligible QFT-G, n=173* QFT-G data incomplete, n=2 QFT-G indeterminate, n=34 (3.4%) QFT-G performed > 14 days before or after clinical evaluation, n=100 QFT-G performed >7 days after treatment initiation, n=57 Active Disease Diagnosed Prior to Initial Evaluation, n=11 Known HIV+, n=52 Age < 10 years, n=9 Final diagnosis of culturenegative TB, n=38 AFB smear-positive, n=57 AFB smear negative active tuberculosis suspects, n=660 Ruled out for active tuberculosis, n=595 Culture-positive tuberculosis, n=65 * Some patients were excluded for more than one reason 14

19 Figure 2a. Changes in Predicted Risk of Active Disease following Incorporation of Quantitative IFN-γ Results, Cases. Predicted Risk of Active Disease, Clinical Model Alone Improvement ---> Predicted Risk of Active Disease, Clinical Model with Addition of QFT-G Results 15

20 Figure 2b. Changes in Predicted Risk of Active Disease following Incorporation of Quantitative IFN-γ Results, Non-cases. Predicted Risk of Active Disease, Clinical Model Alone <--- Improvement Predicted Risk of Active Disease, Clinical Model with Addition of QFT-G Results Dashed diagonal lines represent no change in risk prediction with addition of quantitative IFN-γ results. Among culture-proven cases, individuals to the right of the dashed diagonal line indicate higher (and therefore improved) risk prediction, and those to the left indicate lower (and therefore worse) risk prediction; these criteria are reversed for non-cases. Horizontal and vertical solid lines indicate lower (5%) and upper (20%) risk cut points at ordinate and abscissa, respectively. 16

21 Chapter 2. Interferon-gamma Release Assays for Diagnosis of Active Pulmonary TB Diagnosis in Adults in Low- and Middle-Income Countries: Systematic Review and Meta- Analysis ABSTRACT Background: The value of IGRAs in the diagnosis of active TB in low- and middle-income countries is unclear. Methods: We searched multiple databases for studies published through May 2010 evaluating the diagnostic performance of QuantiFERON-TB Gold In-Tube (QFT-GIT) and T-SPOT.TB (T- SPOT) among adults with or suspected of having active pulmonary TB in low- and middleincome countries. We summarized test performance characteristics using forest plots, hierarchical summary ROC (HSROC) curves, and bivariate random effects models. Results: Our search identified 789 citations, of which 27 observational studies (17 QFT-GIT, 10 T-SPOT) evaluating 590 HIV-uninfected and 844 HIV-infected individuals met inclusion criteria. Among HIV-infected patients, HSROC/bivariate pooled sensitivity estimates (highest quality data) were 76% (95% CI 45-92%) for T-SPOT and 60% (95% CI 34-82%) for QFT-GIT. HSROC/bivariate pooled specificity estimates were low for both IGRA platforms among all subjects (T-SPOT (61%, 95% CI 40-79%), QFT-GIT (52%, 95% CI 41-62%)), and among HIVinfected subjects (T-SPOT (52%, 95% CI 40-63%), QFT-GIT (50%, 95% CI 35-65%)). There was no consistent evidence that either IGRA was more sensitive than the TST for diagnosis of active TB. Conclusions: In low- and middle-income countries, IGRAs are inadequate for ruling out or ruling in active TB, especially in the setting of HIV co-infection. 17

22 BACKGROUND Interferon-gamma release assays (IGRAs) are the first new diagnostic test for latent tuberculosis infection (LTBI) in over 100 years. Newest generation IGRAs measure interferon-gamma (IFNγ) secretion after exposure of whole blood (QuantiFERON-TB Gold In-Tube [QFT-GIT], Cellestis, Carnegie, Australia) or peripheral blood mononuclear cells (T-SPOT.TB [T-SPOT], Oxford Immunotec, Abingdon, UK) to antigens encoded within the region of difference-1 (RD1), a portion of the MTB genome absent among all BCG strains and most nontuberculous mycobacteria. [1] We have shown in previous systematic reviews that, compared to the tuberculin skin test (TST), IGRAs have higher specificity for LTBI in low tuberculosis (TB) incidence settings, better correlation with surrogate measures of M. tuberculosis exposure, and less cross reactivity with response to BCG vaccine. [2-4] In recent years, IGRAs have become widely endorsed in high income-countries for diagnosis of LTBI. [5-7] IGRAs were explicitly designed to replace the tuberculin skin test (TST) in the diagnosis of LTBI, and were not intended to be used in the diagnosis of active TB, which is a diagnosis based on microbiological tests (e.g., culture and microscopic examination of clinical specimens). Furthermore, the diagnosis and treatment of LTBI remains a low priority in most low- and middle-income countries, where detection and management of active TB is the highest priority for national TB control programs. Because IGRAs, like the TST, cannot distinguish LTBI from active TB, [8-10] these tests can be expected to have poor specificity for diagnosis of active TB in all high burden settings, due to a high background prevalence of LTBI. [11] Other differences in patient characteristics, such as anergy due to advanced disease, malnutrition, and HIVassociated immune suppression, or characteristics of the setting, such as laboratory procedures and infrastructure, may also contribute to the observed poorer performance of IGRAs in these settings. [12] Yet, private sector laboratories in high burden countries increasingly employ IGRAs to diagnose active TB, [13] and many investigators continue to recommend the use of IGRAs for this purpose. [14-17] Because of uncertainty of the benefits and costs to patients and national TB programs, we conducted a systemic review and meta-analysis to determine IGRA test performance in patients suspected of having active pulmonary TB and in patients with confirmed TB living in low- and middle-income settings. METHODS Overview. Given the absence of studies evaluating patient-important outcomes among TB suspects randomized to treatment based on IGRA results, we focused our review on the accuracy of IGRAs in diagnosing active TB. We followed standard guidelines and methods for systematic reviews and meta-analyses of diagnostic tests.[18-21] Search methods. We have previously published systematic and narrative reviews on the accuracy and performance of IGRAs in various subgroups.[2-4, 10, 22] We updated the previous literature searches to identify all studies evaluating IGRAs published through May 2010 searching PubMed, Embase, Biosis and Web of Science for studies in all languages. The search terms used included: ((interferon-gamma release assay*) OR (T-cell-based assay*) OR (antigenspecific T cell*) OR (T cell response*) OR (T-cell response*) OR (interferon*) OR (interferon- 18

23 gamma) OR (gamma-interferon) OR (IFN) OR (elispot) OR (ESAT-6) OR (CFP-10) OR (culture filtrate protein) OR (Enzyme Linked Immunosorbent Spot) OR (Quantiferon* OR Quantiferon- TB)) AND (tuberculosis OR mycobacterium tuberculosis). In addition to database searches, we reviewed bibliographies of reviews and guidelines, screened citations of all included studies, searched clinicaltrials.gov for ongoing studies, and contacted both experts in the field and IGRA manufacturers to identify additional published and unpublished studies. We requested pertinent information not reported in the original publication from the primary authors of all studies included in the review. Study selection and data collection. We included studies that evaluated the performance of the most recent generation of commercial, RD1 antigen-based IGRAs (QuantiFERON-TB Gold In- Tube (QFT-GIT) (Cellestis, Victoria, Australia) and T-SPOT (Oxford Immunotec, Oxford, United Kingdom) among adults (aged 15 years) suspected of or having active pulmonary TB in low- and middle-income countries, [23] using the World Bank Country Classification as a surrogate for national TB incidence. HIV infection was established either by documented serological testing or self-report. We excluded: (1) studies that evaluated non-commercial (inhouse) IGRAs, purified protein derivative (PPD)-based IGRAs, QuantiFERON-TB Gold (2G), and IGRAs performed in specimens other than blood; (2) longitudinal data focused on the effect of anti-tb treatment on IGRA response; (3) studies including < 10 eligible individuals; (4) studies focused on extrapulmonary tuberculosis or children (<15 years); (5) studies reporting insufficient data to determine diagnostic accuracy measures; and (6) conference abstracts, letters without original data, and reviews. At least two reviewers (JZM, CE, KRS, AC) independently screened each of the accumulated citations for relevance, reviewed full-text articles using the pre-specified eligibility criteria, and extracted data using a standardized form. The reviewers resolved disagreements about study selection and data extraction by consensus. Assessment of study quality. Because primary outcomes for this systematic review focus on test accuracy, we evaluated study quality using a subset of relevant criteria from QUADAS (a validated tool for diagnostic accuracy studies). [24] Because of growing concerns about conflicts of interest in diagnostic studies and guidelines, [25, 26] we also report whether IGRA manufacturers had any involvement with the design or conduct of each study, including donation of materials, monetary support, work/financial relationships with study authors, and participation in data analysis. Outcome Definitions. Well-designed diagnostic accuracy studies focus on a representative target population in whom genuine diagnostic uncertainty exists (i.e., patients in whom clinicians would apply the test in the course of regular clinical practice). [27] There is evidence that diagnostic studies that include only known cases with the condition of interest and healthy controls without this condition tend to overestimate test accuracy. [28] Therefore, we considered studies simultaneously evaluating IGRA sensitivity and specificity among active TB suspects to represent the highest quality evidence, while studies evaluating IGRA performance among patients with known active TB (for sensitivity) to be of lesser quality. Because of our focus on the diagnostic accuracy for active TB and the high prevalence of LTBI in high TB burden settings, IGRA specificity was estimated exclusively among studies enrolling active TB suspects where the diagnostic workup ultimately showed no evidence of active disease. 19

24 A hierarchy of reference standards for active TB was developed a priori to judge the quality of each individual assessment of IGRA diagnostic accuracy. From most to least favorable, these reference standards included 1) culture-confirmation or sputum smear-positivity in high TB incidence settings ( 50/100,000), where sputum smear microscopy has been shown to have high specificity; [29] 2) sputum smear-positivity without culture in low or intermediate TB incidence settings (<50/100,000); and 3) clinical diagnosis based upon presenting symptoms, radiologic findings and/or response to TB treatment without microbiological confirmation. Because the TST remains in widespread use, and because indeterminate IGRA results may affect assay performance in low income settings, we also evaluated (1) observed differences in sensitivity for active TB diagnosis between IGRA and TST, and (2) the proportion of IGRA results among patients with active TB that are indeterminate. We used the following definitions for primary outcomes: (1) Sensitivity the proportion of individuals with a positive IGRA result among those with culture-positive TB (we included indeterminate IGRA results in the denominator if they occurred in individuals with culturepositive TB); and (2) Specificity the proportion of individuals with a negative IGRA result among those ruled out for active TB disease (indeterminate IGRA results were excluded from analysis). Using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework,[27] these measures can be interpreted as surrogates for patient-important outcomes. Data synthesis and meta-analysis. Multiple sources of heterogeneity commonly exist when summarizing estimates from studies of diagnostic tests. [30] We adopted the following approach to account for expected heterogeneity. First, when possible, we separately synthesized data for each commercial IGRA and by HIV status. The pre-specified sub-groups minimize heterogeneity related to differences in testing platform (ELISA vs. ELISPOT), antigens used to elicit IFN-g release (ESAT-6/CFP-10 vs. ESAT-6/CFP-10/TB 7.7), and test performance related to HIVassociated host immunosuppression. Second, we visually assessed heterogeneity using forest plots, characterized the variation in study results attributable to heterogeneity (I-squared value), and statistically tested for heterogeneity (chi-squared test). [30] Third, we calculated pooled sensitivity and specificity estimates using random effects modeling, which provide more conservative estimates than fixed effects modeling when heterogeneity is a concern. [19, 31] For each individual study, we assessed all outcomes for which data were available. First, we generated forest plots to display the individual study estimates and their 95% confidence intervals. Second, we used bivariate random effects regression models [32] when both sensitivity and specificity could be reported from the same TB suspect population. Because pooling sensitivity and specificity separately can produce biased estimates of test accuracy [19], we preferred to generate pooled estimates when both sensitivity and specificity were reported within a study and ranked this as higher quality evidence. Third, we generated hierarchical summary receiver operating characteristic (HSROC) curves to summarize the global test performance.[31] Because of the need to summarize two correlated measures (e.g., sensitivity and specificity), and because substantial between-study heterogeneity is common, meta-analysis of diagnostic accuracy requires different and more complex methods than traditional metaanalytic techniques. Graphically illustrating the trade-off between sensitivity and specificity, HSROC curves differ from traditional Receiver Operating Characteristics (ROC) curves in 20

25 allowing accuracy to vary by each individual study (i.e., allowing for random effects, and thus allowing asymmetry in the plotted curve), and by discouraging extrapolation beyond the available data by plotting the curve only over the observed range of test characteristics. The HSROC approach is closely related to the bivariate random effects regression model [33]. These two methods generally produce similar results and are both recommended by the Cochrane Diagnostic Test Accuracy Working Methods group. [20] We calculated pooled estimates when at least four studies were available in any sub-group and summarized individual study results when fewer than four studies were available. We performed all analyses using Stata 11 (Stata Corporation, College Station, Texas, USA). For bivariate random effects regression and HSROC analyses, we used the user-written "metandi" program for Stata. [32] RESULTS Search results. The initial search yielded 789 citations (Figure 1). After full-text review of 168 papers, 19 papers [15, 17, 34-50] were determined to meet eligibility criteria for IGRA evaluation of active TB in low- and middle-income settings. Because some papers included more than one commercial IGRA, there were 27 unique evaluations (referred to as studies) 17 of QFT-GIT, and 10 of T-SPOT that included a total of 590 HIV-uninfected and 844 HIVinfected individuals. Study characteristics. Of the total studies, seven (26%) were from low-income countries, and 20 (74%) were from middle-income countries. Fourteen studies (52%) included HIV-infected individuals, and 21 (78%) studies involved ambulatory subjects (i.e., outpatients as well as hospitalized patients) (Table 1). IGRAs were performed in persons suspected of having active TB in 14 (52%) studies, [35, 37-39, 41, 42, 47, 48, 50] and in persons with known active TB in 13 (48%) studies. [15, 17, 34, 36, 40, 44-46, 49, 51] A list of excluded studies and reasons for exclusion is available from the authors upon request. Study quality. The majority of studies satisfied the QUADAS criteria assessed (Figure 2), with the exception of patient spectrum (biased sampling) and blinding. Sixteen (59%) studies did not enroll a representative spectrum of patients, and nine (33%) studies did not clearly report whether assessment of the reference standard was performed blinded to IGRA results. Industry involvement was unknown in five (19%) studies and acknowledged in eight (30%) studies, including donation of IGRA kits (6 studies) and work/financial relationships between authors and IGRA manufacturers (2 studies). Sensitivity and specificity estimation among TB suspects. We identified a total of 14 studies that simultaneously estimated sensitivity and specificity in TB suspects, and test accuracy estimates were pooled using bivariate random effects/hsroc methods (these studies were ranked as high quality evidence). Overall, studies enrolling active TB suspects demonstrated a sensitivity of 83% (95% CI 63-94%) and specificity of 61% (95% CI 40-79%) for T-SPOT (6 studies), and a sensitivity of 69% (95% CI 52-83%) and specificity of 52% (95% CI 41-63%) for QFT-GIT (8 studies). Sensitivity. With the exception of two studies, [37, 48] the sensitivity of IGRAs was assessed based on a positive culture result (21 studies, 78%) or a positive sputum AFB-smear result in a high TB incidence setting (4 studies, 15%). Among studies performed in patients with known active TB, 6 (46%) included patients who had been treated for greater than one week. 21

26 HIV-infected Nine studies assessed IGRA sensitivity among HIV-infected active TB suspects. HSROC/bivariate pooled sensitivity estimates were higher for T-SPOT (76%, 95% CI 45-92%, 4 studies [35, 38, 41, 42]) than for QFT-GIT (60%, 95% CI 34-82%, 5 studies [38, 39, 41, 42, 50] (Figure 3). Pooled sensitivity estimates did not change appreciably for either T-SPOT (68%, 95% CI 56-80%, 5 studies [15, 35, 41-43]) or QFT-GIT (65%, 95% CI 52-77%, 7 studies [34, 39, 41, 42, 49, 50]) when studies evaluating patients with known active TB were included in the analysis (Figure 4). Pooled sensitivity estimates for both T-SPOT (I-squared 72%, p<0.01) and QFT-GIT (I-squared 76%, p<0.001) showed significant heterogeneity. HIV-uninfected Five studies assessed IGRA sensitivity among HIV-uninfected active TB suspects; data were insufficient to report HSROC/bivariate pooled sensitivity estimates for either QFT-GIT [37, 38, 48] or T-SPOT [38, 47]. Pooled sensitivity estimates were similar for T-SPOT (88%, 95% CI 81-95%, 4 studies [17, 38, 44, 47]) and QFT-GIT (84%, 95% CI 78-91%, 9 studies [10, 34, 36-38, 40, 46, 48, 49]) when studies evaluating patients with known active TB were included in the analysis (Figure 5). Pooled sensitivity estimates showed significant heterogeneity for QFT-GIT (I-squared 60%, p=0.01), but not for T-SPOT (I-squared 28%, p=0.25). Head-to-head comparisons of QFT and T-SPOT sensitivity Overall, four studies (three involving HIV-infected subjects [38, 41, 42] and one involving HIVuninfected subjects [38]) reported head-to-head comparisons of T-SPOT and QFT-GIT sensitivity. T-SPOT sensitivity was higher but not significantly different from QFT-GIT sensitivity (sensitivity difference 19%, 95% CI -17% to 56%, p=0.3) (Table 2). Results were similar when restricted to HIV-infected individuals. Head-to-head comparison of TST and IGRA sensitivity Overall, nine studies reported head-to-head comparisons of TST and IGRA (3 T-SPOT and 6 QFT-GIT) sensitivity. TST sensitivity in the five studies [17, 40, 44, 46, 49] involving HIVuninfected patients was higher (78%, 95% CI 71-86%) than in the four studies [15, 39, 46, 49] involving HIV-infected patients (45%, 95% CI 15-75%). IGRA sensitivity was not statistically different than TST sensitivity for either T-SPOT (sensitivity difference 23%, 95% CI 0% to 45%, p=0.05) or QFT-GIT (sensitivity difference 7%, 95% CI -9% to 23%, p=0.37) (Figure 6). There was significant heterogeneity for both estimates (I-squared >75%, p<0.001). Data were insufficient to form HIV-stratified pooled sensitivity difference estimates for either IGRA. Specificity. All specificity estimates were determined in TB suspects using HSROC/bivariate techniques. Overall, pooled specificity was low for both T-SPOT (61%, 95% CI 40-79%, 6 studies) and QFT-GIT (52%, 95% CI 41-62%, 8 studies). When restricted to HIV-infected active TB suspects, pooled specificity for T-SPOT (52%, 95% CI 40-63%, 4 studies [35, 38, 41, 42]) was similar to QFT-GIT (50%, 95% CI 35-65%, 5 studies [38, 39, 41, 42, 50]) (Figure 3). Too few studies were available to estimate pooled specificity for HIV-uninfected patients. 22

27 Proportion of Indeterminate IGRA Results. The proportion of indeterminate IGRA results among patients with suspected or confirmed active TB varied considerably (range 0-26% among studies enrolling 50 or more subjects). The proportion of indeterminate results was low (4%, 95% CI 1-7%) among HIV-uninfected patients, regardless of IGRA platform (Supplementary Figure 1). However, the proportion of indeterminate results was considerably higher among HIV-infected subjects for both QFT-GIT (15%, 95% CI 9-21%, 8 studies) and T-SPOT (9%, 95% CI 0-17%, 6 studies) (Supplementary Figure 2). Results were similar for HIV-infected subjects when stratified by TB suspects versus known TB cases. DISCUSSION The vast majority of the estimated annual 9.3 million new cases of active TB and 1.3 million TBrelated deaths occur in low- and middle-income countries. [52] Due to resource constraints, public health policies have appropriately placed limited emphasis on diagnosis and treatment of LTBI in these settings. Clinical use of IGRAs, however, has expanded dramatically in recent years, especially in the private sector. [13] Because of their high burden of disease and emerging economies, such countries (e.g., India, South Africa, Brazil and China) represent a potentially lucrative market for manufacturers of commercial IGRAs. Even though IGRAs are intended to be used to diagnose LTBI and not to diagnose active TB, and even though they cannot distinguish between latent infection and active disease, there is increasing use of IGRAs to diagnose active TB in high burden countries. In this systematic review focused on individuals living in low- and middle-income countries, the highest quality evidence concerning the accuracy of IGRAs among TB suspects demonstrated sensitivity ranging from 69-83% and specificity ranging from 52-61%. Further, there was no consistent evidence that either IGRA was more sensitive than the TST for active TB diagnosis. The majority of evidence on the diagnostic accuracy of IGRAs to date has come from high income settings, where active TB has been used as a surrogate reference standard when estimating the accuracy for LTBI diagnosis. [4, 14] Yet, diagnostic test performance (e.g., sensitivity and specificity) can be expected to vary according to disease prevalence and other population characteristics. [53, 54] Likewise, clinicians have been advised to base their decisionmaking on studies that most closely match their own clinical circumstances. [55] IGRAs were designed to be used as diagnostic tests of LTBI, a setting in which the lack of an accepted gold standard has been a significant limitation in establishing test performance. By contrast, adequate and commonly used reference standards exist for diagnosing active TB. Among studies that enrolled active TB suspects (i.e., patients with diagnostic uncertainty), both IGRAs demonstrated suboptimal rule-out value for active TB. In other words, one in four patients, on average, with culture-confirmed active TB can be expected to be IGRA-negative in low and middle income countries - this has consequences for patients in terms of morbidity and mortality if treatment decision sare made based on such results. Although high quality data were limited, sensitivity of both IGRAs was lower among HIV-infected patients (~60-70%), suggesting that nearly one in three HIV-infected patients with active TB will be IGRA-negative. The few available head-to-head comparisons between QFT-GIT and T-SPOT demonstrated higher sensitivity for the T-SPOT platform, though this difference did not reach statistical significance. Lastly, comparisons with pooled estimates of TST sensitivity were difficult to 23

28 interpret due to substantial heterogeneity. Our results, however, suggest that neither IGRA platform may be more sensitive than the TST in diagnosis of active TB in low- and middleincome countries. The specificity of IGRAs in diagnosing LTBI, estimated among individuals at low risk for TB exposure in low TB incidence (high income) settings, is known to be high ( 98%). [4] In contrast, specificity for active TB diagnosis is best estimated only within studies evaluating TB suspects. As expected, due to the higher background prevalence of LTBI and the known inability of IGRAs to differentiate LTBI from active TB, [10] the specificity of both IGRAs for active TB was low, regardless of HIV infection status. These data suggest that one in two patients without active TB will be IGRA-positive - this has consequences for patients because of unnecessary therapy for TB and its attendant risks. Our findings are intuitive and biologically plausible, because it is well known that T-cell IFN-γ responses are activated in nearly the entire spectrum of TB infection, from latency to active disease, [56] and currently available tests cannot distinguish LTBI from active TB disease. Even within the spectrum of latent TB infection itself, [57] actuated T-cell IFN- γ responses occur throughout each phase, with the possible exception of the innate immune response (which eliminates M. tuberculosis without priming a T-cell immune response). The goal of our systematic review was to critically evaluate the diagnostic accuracy of IGRAs in the diagnosis of active TB in low and middle income settings. Yet, there are inherent limitations to sensitivity, specificity, and predictive values as measures of test performance. These measures are unable to determine either the extent to which a test may improve on readily available clinical information [58] or the degree to which patient-important outcomes are improved by test results. [27] Although limited, available data suggest that IGRAs may add little information to the conventional diagnostic work-up for active TB in low [59] and high TB incidence settings [60]. Further work is necessary to confirm this finding. Our meta-analysis had several limitations. First, as with previous systematic reviews, [4, 14] heterogeneity was substantial for the primary outcomes of sensitivity and specificity. We utilized empirical random effects weighting, excluded all studies contributing fewer than ten eligible individuals, and separately synthesized data for currently manufactured IGRAs in order to minimize heterogeneity. Second, World Bank income classification is an imperfect surrogate for national TB incidence. Although no standard criteria currently exist for defining high TB incidence countries, our results were fundamentally unchanged when restricted to nations with an arbitrarily chosen annual TB incidence of greater than or equal to 50/100,000. [52] Third, it is likely that unpublished data and ongoing studies were missed. It is also possible that studies that found poor IGRA performance were less likely to be published. Given the lack of statistical methods to account for publication bias in diagnostic meta-analyses, it would be prudent to assume some degree of overestimation of our estimates due to publication bias. Fourth, our review did not include evidence on utility of IGRAs in two patient subgroups where conventional tests for active TB perform poorly - children and patients with suspected extrapulmonary TB. Lastly, we did not identify any studies directly measuring the impact of IGRA test results on patient-important outcomes. 24

29 In conclusion, as in the case of the TST, the data suggest no role for using IGRAs in the diagnosis of active TB among adults living in low- and middle-income countries. These data should help inform evidence-based policies on the role of IGRAs in active TB diagnosis in lowand middle-income settings. Indeed, a World Health Organization (WHO) Expert Group considering this evidence recently recommended that IGRAs not be used as a replacement for conventional microbiological diagnosis of pulmonary and extra-pulmonary TB in low-and middle-income countries.[61] 25

30 TABLES AND FIGURES Table 1. Characteristics of Included Studies. Study, Year Country Income Setting Total Patients, n Active TB n (%) Indeterminate n (%) Industry Involvement QFT-GIT Aabye 2009 Tanzania Low Inpatient/Outpatient HIV (100) 8 (9) Work relationship Aabye 2009 Tanzania Low Inpatient/Outpatient HIV (100) 15 (22) Work relationship Raby 2008 Zambia Low Outpatient HIV (100) 5 (14) No Raby 2008 Zambia Low Outpatient HIV (100) 10 (17) No Chegou 2009 South Africa Upper Middle Outpatient HIV (100) 0 (0) No Chen 2009 China Lower Middle NR HIV (84) 2 (4) Unclear Dheda (b) 2009 South Africa Upper Middle Inpatient/Outpatient HIV (25) 8 (40) No Dheda (d) 2009 South Africa Upper Middle Inpatient/Outpatient HIV (29) 14 (27) No Kabeer 2009 India Lower Middle Inpatient/Outpatient HIV (69) 12 (19) No 26

31 Katiyar 2008 India Lower Middle Outpatient HIV (100) 0 (0) Unclear Leidl (b) 2009 Uganda Low Outpatient HIV (15) 4 (3) Kit Donation Markova (b) 2009 Bulgaria Upper Middle Outpatient HIV (14) 5 (6) No Pai 2007 India Lower Middle Inpatient/Outpatient HIV (100) 0 (0) Unclear Tahereh 2010 Iran Lower Middle Unclear HIV (35) 6 (7) Unclear Tsiouris 2006 South Africa Upper Middle Outpatient HIV (100) 0 (0) Kit Donation Tsiouris 2006 South Africa Upper Middle Outpatient HIV (100) 5 (19) Kit Donation Veldsman 2009 South Africa Upper Middle Outpatient HIV (50) 9 (15) No T-SPOT.TB Cattamanchi 2010 Uganda Low Inpatient HIV (53) 54 (25) Kit Donation Dheda (a) 2009 South Africa Upper Middle Inpatient/Outpatient HIV (25) 1 (5) No 27

32 Dheda (c) 2009 South Africa Upper Middle Inpatient/Outpatient HIV (31) 2 (4) No Jiang 2009 China Lower Middle Inpatient/Outpatient HIV (100) 0 (0) No Leidl (a) 2009 Uganda Low Outpatient HIV (15) 6 (5) Kit Donation Markova (a) 2009 Bulgaria Upper Middle Outpatient HIV (14) 9 (10) No Oni 2010 South Africa Upper Middle Outpatient HIV (100) 5 (6) Kit Donation Ozekinci 2007 Turkey Upper Middle Inpatient HIV (100) 0 (0) No Soysal 2008 Turkey Upper Middle Inpatient HIV (97) 4 (4) No Shao-ping 2009 China Lower Middle Inpatient HIV (27) 6 (7) No Definition of abbreviations: IQR, interquartile range; LTBI, latent tuberculosis infection; TB, tuberculosis; NR, not reported. * Unpublished studies. Indeterminate results were not excluded in calculating sensitivity estimates. Kit donation refers to donation of any test materials including kits and reagents. Work relationship refers to when one or more authors are involved in test development, consulting work, or other employment by an IGRA manufacturer. 28

33 Table 2. Head-to-Head Comparison of Sensitivity of T-SPOT.TB versus QuantiFERON-TB Gold In-Tube among Active Tuberculosis Suspects. Author, Year Country HIV Status Positive T- Positive QFT Sensitivity Active TB, SPOT Result, Result, difference* n (%) n (%) n (%) (%) Dheda, 2009 South Africa HIV- 15 (31), 15 (29) 14 (93) 11 (73) 20 Dheda, 2009 South Africa HIV+ 5 (25) 5 (100) 1 (20) 80 Leidl, 2009 Uganda HIV+ 19 (15) 17 (89) 14 (74) 15 Markova, 2009 Bulgaria HIV+ 13 (14) 8 (62) 12 (92) -31 * Sensitivity difference (%) is T-SPOT sensitivity (%) QFT-GIT sensitivity (%). Total numbers of active TB suspects evaluated by each IGRA differed within some studies; these are listed in the order T-SPOT, QFT-GIT. 29

34 Titles/abstracts identified and screened for full-text retrieval: 789 Excluded based on title and abstract: 621 Added from prior systematic review: 17 Full papers retrieved for more detailed evaluation: 168 Excluded: 134 Reasons Children: 1 Duplicate data: 2 Extrapulmonary TB: 2 Less than 10 TB patients: 3 LTBI: 93 Noncommercial IGRA: 8 Nonstandard IGRA method: 3 Obsolete IGRA (QFT-Gold): 22 Pulmonary TB, all countries: 51 High income countries: 32 Pulmonary TB, Low/middle income countries: 19 papers (27 studies) Figure 1. Study selection. Abbreviations: IGRA, interferon-gamma release assay; LTBI, latent tuberculosis infection; TB, tuberculosis 30

35 Representative spectrum? Acceptable reference standard? Acceptable delay from index to reference test? Partial verification avoided? Differential verification avoided? Incorporation avoided? Index test results blinded? Reference standard blinded? Relevant clinical information? Uninterpretable results reported? Withdrawals explained? Industry involvement lacking? 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Yes Unclear No Figure 2. Assessment of study quality using the QUADAS tool. For each QUADAS item, two reviewers independently determined whether a study did or did not meet the quality criterion, or whether it was unclear. 31

36 Figure 3a-b. Hierarchical Summary Receiver Operating Characteristics (HSROC) Plot of Studies that Reported both Sensitivity and Specificity in Active TB Suspects. The summary curves from the HSROC model contain a summary operating point (red square) representing summarized sensitivity and specificity point estimates for individual study estimates (open circles). The 95% confidence region is delinieated by the area within the orange dashed line.

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